This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several MCTS variants where the conventional random simulation is replaced by other simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess these combined MCTS variants. We provide empirical evidence to shed light on the advantages and limits of each simulation strategy. This is an extension of the work of Grelier and al. presented at EvoCOP2022.

翻译：本文研究了蒙特卡洛树搜索方法结合专用启发式算法求解加权顶点着色问题。除了基础MCTS算法外，我们探讨了多种MCTS变体，其中传统的随机模拟被其他模拟策略（包括贪婪算法和局部搜索启发式）所取代。我们在知名基准实例上开展实验，评估这些混合MCTS变体的性能。我们提供实证分析，阐明每种模拟策略的优势与局限性。本文是Grelier等人在EvoCOP2022所发表工作的延伸。